The Effect of Switching on Compliance and Persistence: The Case of Statin Treatment

Published Online: November 01, 2005
Patrick Thiebaud, PhD; Bimal V. Patel, PharmD; Michael B. Nichol, PhD; and David M. Berenbeim, MD

Objective: To determine the effect of switching drugs on the compliance and persistence of new statin users.

Study Design: Retrospective database analysis of pharmacy claims provided by a large pharmacy benefit manager. The study sample consisted of 38 866 new statin users 18 to 65 years old beginning treatment with atorvastatin calcium, fluvastatin sodium, lovastatin, pravastatin sodium, or simvastatin.

Methods: Compliance was measured by the "medication possession ratio," and persistence was measured by the time to discontinuation. Switching rates were derived from the proportions of patients filling a prescription other than the initial statin.

Results: Patients who switched statins were less compliant by 18.9% (odds ratio, 0.81; P < .001), as defined by the probability of having a medication possession ratio of 0.8 or higher, and were less persistent by 20.9% to 48.3% (P < .001) depending on the gap length used to define discontinuation.

Conclusions: Switching statins substantially reduces the likelihood that patients will be compliant and remain on treatment long enough to obtain the full benefit of statin treatment. To ensure better compliance, special care should be given to patients who change drugs.

(Am J Manag Care. 2005;11:670-674)

Coronary heart disease and stroke impose a large burden on individual patients and society. In 2001, coronary heart disease and stroke had prevalences of 6.4% and 2.0%, respectively,1 and their combined projected cost in 2005 is $142.1 billion.2 Since the National Cholesterol Education Program (NCEP) issued in 2001 its third report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III),3 other investigations have suggested that a more aggressive approach should be taken in cholesterol management, targeting lower levels of low-density lipoprotein (LDL) cholesterol.4 These 2004 guidelines recommend a low-density lipoprotein cholesterol level of less than 100 mg/dL (<2.59 mmol/L). Regardless of their initial LDL cholesterol levels, patients should initiate therapeutic lifestyle changes first, followed by drug therapy if additional reduction is needed. Statins are recommended as the first-line drug therapy because of their effectiveness and good tolerability,3 but nonstatin options such as bile acid sequestrant or nicotinic acid are also available.

Statins have been shown to reduce the number of vascular events when used in primary and secondary prevention of coronary heart disase.5-8 They also contribute to a lower incidence of stroke.1,2 Despite the weight of evidence supporting sustained treatment, nonadherence and suboptimal adherence remain common problems among patients treated with statins.9-14 Poor compliance not only deprives patients of the benefit of treatment but also can lead to serious adverse events.15-17

Compliance and persistence depend partially on how successful the treatment is at lowering cholesterol to a target concentration and on what the patient perceives to be the health benefit.18,19 Treatment adverse effects are also a major source of low compliance, as 41% of patients discontinue therapy because of poor efficacy or adverse events.20

Prescribing suboptimal initial drugs and maintaining patients on low dosages for too long, among other inappropriate therapies, reduce the chance that treatment will succeed in reaching cholesterol targets.21 Once drug treatment has started, LDL cholesterol control may require higher dosages of the initial drug, additional lipid-lowering agents, or a switch to another statin, a therapeutic step justified by variation in the tolerability and efficacy of statins.22-24

In this context, it is critical to determine what effect switching statins has on compliance and whether a drug change compromises treatment goals. A better knowledge of statin utilization patterns in clinical practice would clarify the options available to physicians for improving lipid management and would allow them to better understand the consequences of altering drug treatment. To our knowledge, no published research has focused on this issue. This research seeks to fill this void by estimating how switching statins affects compliance and persistence among new statin users in a managed care environment.


Study Sample and Selection Criteria

We conducted a retrospective cohort study based on data provided by a West Coast pharmacy benefit manager. Patients were between the ages of 18 and 65 years. All patients were new statin users as defined by a 1-year wash-out period before filling the first prescription for a statin. Each patient had been continuously eligible for benefits for 2 years between April 2001 and June 2004, specifically for 1 year before and 1 year after filling the first statin prescription. The beginning date for continuous eligibility varied among patients. The date of first prescription fill defined the index date for the analysis.

Outcome Measures

Compliance and persistence were measured for the following 5 statins: atorvastatin calcium, fluvastatin sodium, lovastatin, pravastatin sodium, and simvastatin. (Rosuvastatin calcium was introduced too recently to allow for evaluation of compliance within the time frame of the analysis.)

Compliance was measured by the number of days the medication was available to the patient, called the "medication possession ratio"(MPR). The MPR is the sum of the days of supply for 1 year after the first prescription was filled, divided by 360. Any prescriptions filled later than 360 days after the index date were excluded, as were any days of supply that occurred after the end of follow-up. The MPR calculation included all statins after the index date, even if different from the initial drug.

Persistence represents the length of time patients remained supplied with statins. It was measured by the time to discontinuation, defined as the number of days before the first gap in medication possession. A gap is the number of days between the time patients ran out of their current prescription and the time they filled a new prescription. Before reaching this threshold, patients were assumed to be fully compliant. The supply of overlapping statin prescriptions was added to the total days of continuous supply. Results are provided herein for gap lengths of 15, 30, and 60 days.

Finally, we defined indicators of switching statins and of increasing the dosage. We used binary indicators for switching to another statin during the 1-year followup and for increasing the dosage from the strength of the initial statin.


All predictors were used as covariates in the multivariate analyses. They were divided into 4 categories. First, we defined predictors related to patients, including age, sex, geographic region, and health status as determined by RxRisk, a risk assessment system that classifies patients by their medication use during the year before their statin index date.25 Second, we defined predictors related to health insurance, including formulary types, health plan sizes, and mean copayments. There are 3 formulary types, ranked from the most to the least restrictive, namely, closed, incentive, and open types. A closed formulary does not cover nonformulary drugs, an incentive formulary covers nonformulary drugs but at a higher copayment, and an open formulary covers nonformulary drugs at the same copayment. The health plan size variables were divided into the following categories based on the total number of enrollees: fewer than 50 000, 50 000 to 200 000, and more than 200 000. The mean copayments were calculated by averaging out-of-pocket costs across all prescriptions filled before switching statins if there was a switch and across all prescriptions otherwise. Third, to control for the effect of complex drug treatment and possible drug interactions on adherence, we defined variables related to drugs used after the initial statin. These variables included the utilization of nonstatin antilipidemic agents, the use of antihypertensive drugs for cardiovascular disease (antiarrythmics, vasodilators, and cardiac stimulants), and the number of prescriptions per month before the switch for switchers and during the whole year for nonswitchers. We also accounted for the use of nonstatin antilipidemic agents before treatment initiation. Fourth, the year of treatment initiation was included in our estimates to remove the effect of treatment trends and stricter treatment guidelines.

Statistical Analysis

One of the challenges of observational studies is to distinguish the effect of treatment from the effect of factors that influence treatment selection. Without random assignment, treatment groups may differ in ways that influence treatment choice and treatment outcomes, thereby introducing bias in the estimation of treatment effect. In this study, it was important to account for the differences that may affect outcomes between patients who do and do not switch drugs. To control for this problem, the propensity score (PS) method was used to adjust for pretreatment differences between treatment groups.26 Controlling for potential bias and adjusting for pretreatment differences require that variables determining selection, such as alternative treatments, be included in the estimation of the PS as controls. The PS is a patient's estimated probability of switching drugs as calculated by logistic regression of all pretreatment variables (age, sex, health status, mean copayment, use of nonstatin antilipidemic agents, etc) on a binary variable equal to 1 if the patient switched drugs. The inverse of the PS is then used to weigh each observation in other logistic regressions. Propensity score-weighted logistic regressions were used to evaluate the effect of treatment on the probability of compliance (ie, having an MPR of ≥ 0.8) and different degrees of low compliance (ie, having MPRs of 0.4-0.8 and < 0.4). Other PSweighted logistic regressions were used to estimate the probability of the time to discontinuation (ie, having first gaps in treatment of ≥ 180 and ≥ 360 days). Analyses were performed with SAS 9.1 (SAS Institute Inc, Cary, NC).


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